This thesis addresses the problem of target positioning and localization using Radio Frequency (RF) based measurements and using a variety of modulation including Time of Arrival (ToA), Phase of arrival (PoA) and Received Strength Indicator of RF signals (RSSI). Starting from finding the planar coordinates of a device from a collection of ranging measurements using weighted least square (WLS) methods, we explore the dependency of the solution uncertainty from the geometric configuration of anchors and then develop solutions that compensate for the effects of geometry and reduce the positioning uncertainty to a value close to the Cramer–Rao Lower Bound (CRLB), a measure which is then used in the proceeding chapters for developing optimal anchor configurations for positioning problem with guaranteed estimation uncertainties. The findings in the positioning part are also used to address the limitations of initializing Ultra-Wideband (UWB) anchors through a random trajectory. This is done by studying the dual of the positioning problem addressed in the first part, that is incorporating CRLB as a measure of optimality to design a trajectory that minimizes the uncertainty of anchor initialization. We finally close the positioning part of the thesis by studying the range and bearing measurements provided by radar sensors for people tracking and positioning in indoor environments. Taking into account the target dynamics, in the second part of the thesis we present observabilty analysis and localization for non-holonomic robots, using a combination of onboard sensors and range-only anchors. By using a discrete-time formulation of the system’s kinematics, we identify the geometric conditions that make the system globally observable and thereby derive the observability-based filter (ObF) to outperform the limitations of the classic Bayesian filters. We then use the implications of this analysis to design an active control and optimal path-planning strategy with guaranteed maximum observability. We close this part of the thesis by investigating localization in presence of intermittent measurements and discuss how the observability of a trajectory can be quantified by the condition number of the system matrix, a subject related to the maneuvers executed by the robot and to the sampling time used to collect the measurements. Eventually, in the last part of this thesis, we address the localization in presence of offset and ambiguities in measurements. First, we show that, while using range-only measurements corrupted with offset, the trajectories can be observed and the offset can be estimated in a finite number of steps. Next, we present an approach to resolve the ambiguity of rang-only measurements obtained from RSSI values at the Ultra-High Frequency (UHF) band by proposing an optimization algorithm that merges RFID and odometry data to reconstruct the entire robot trajectory. Finally, we present a solution to resolve the ambiguity of the RFID signal phase and reconstruct the robot trajectory through sensor fusion and using UHF-RFID passive tags.

In this thesis we address the constructibility problem for a ground vehicle moving across an environment instrumented with ranging sensors. When the measurements collected by the vehicle along the trajectory are sufficiently informative, the global constructibility property is achieved and the vehicle is able to localise itself in the environment without relying on prior information on its state. When this condition is not met, the system can still achieve local (or weak) constructibility, where localising the robot requires some initial information on the state, such as a sufficiently small set containing the initial position of the robot, or some inaccessible areas of the Cartesian plane. First, we address the global problem: we show that extending the well--known solutions for the positioning problem, e.g. trilateration, is not trivial and leads to unintuitive results where constructibility is not attained. By building an abstract trajectory, which contains all the relevant information to reconstruct the actual trajectory followed by the vehicle, we analyse how global constructibility properties are affected by the shape of the abstract trajectory, the number of sensors, their deployment in the environment, and the distribution of measurements among the beacons. To describe local constructibility, we build the Constructibility Gramian for a robot described by the unicycle kinematic model. We rely on this tool for a twofold aim: (a) we build the same abstract trajectory presented for the global analysis and define necessary and sufficient conditions to attain local constructibility, and (b) in an environment instrumented with two beacons and for straight trajectories followed by the vehicle, we measure local constructibility by means of the smallest eigenvalue of the Constructibility Gramian, and we analyse how this metric is affected by the geometry of the scenario, e.g. the distance between anchors, and the distance between the trajectory and the line joining the anchors. Lastly, we extend the devised results to multiagent systems, both for constructibility analysis and for trajectory planning algorithms. We build the Constructibility Gramian for the multiagent system with relative ranging measurements and assess local constructibility property. Then, we propose a trajectory planning algorithm where a pair of vehicles without a priori information achieve global constructibility with both absolute and relative measurements. Moreover, we propose a variation of the Constructibility Gramian, limited to the position variable and hence called Position Gramian, and use this tool in a Model Predictive Control framework to plan the trajectory of a tracker vehicle aiming at simultaneously localising itself and a collaborative target through ranging measurements.

The modern day power grid is a highly complex system; as such, maintaining stable operations of the grid relies on many factors. Additionally, the increased usage of renewable energy sources significantly complicates matters. Attempts to assess the current stability of the grid make use of several key parameters, however obtaining these parameters to make an assessment has its own challenges. Due to the limited number of measurements and the unavailability of information, it is often difficult to accurately know the current value of these parameters needed for stability assessment. This work attempts to estimate three of these parameters: the Inertia, Topology, and Voltage Phasors. Without these parameters, it is no longer possible to determine the current stability of the grid. Through the use of machine learning, empirical studies, and mathematical optimization it is possible to estimate these three parameters when previously this was not the case. These three methodologies perform estimations through measurement-based approaches. This allows for the obtaining of these parameters without required system knowledge, while improving results when systems information is known. / Ph. D. / Stable grid operations means that electricity is supplied to all customers at any given time regardless of changes in the system. As the power grid grows and develops, the number of ways in which a grid can lose stability also grows. As a result, the metrics that are used to determine if a grid is stable at any given time have grown increasingly complex and rely on significantly more amounts of information. This information required in order to obtain the metrics which determine grid stability often has key limitations in when and how it can be obtained. The work presented details several methods for obtaining this information in situations were it was previously not possible to do so. The methods are all measurement based, which means that no prior knowledge about the grid is required in order to compute the values.

As cloud-native applications become more distributed, complex, and unpredictable with the adoption of microservices and other new architectural components, traditional monitoring solutions are inadequate in providing end-to-end visibility and proactively identifying deviations from expected behaviour before they become disruptive to services. In response to these challenges, observability-driven development (ODD) is proposed as a new methodology that leverages tools and practices to observe the state and detect the behaviour of systems. Unlike the leading IT giants developing their proprietary tools and platforms, some non-IT companies and smaller organizations still have difficulty adopting observability-driven development. Proprietary development demands extensive resources and manpower, while connecting to third-party platforms may compromise data security. This thesis proposed an end-to-end observability pipeline that is composed of merely open-source components. The pipeline collects and correlates metrics, logs, and traces to facilitate software development and help troubleshoot in production. The pipeline is designed to be adaptive and extensible so that companies can adopt it as the first step towards observability-driven development, and customize it to meet their specific requirements. / Molnbaserade applikationer blir alltmer distribuerade, komplexa och oförutsägbara med införandet av mikrotjänster och andra nya arkitektoniska komponenter. Detta resulterar i att traditionella övervakningslösningar blir alltmer inadekvata. De traditionella lösningarna tillhandahåller inte tillräcklig överskådlighet över dessa applikationer (end-to-end) för proaktiv identifiering av avvikelser från förväntat beteende innan de börjar påverka tjänsterna negativt. Som svar på dessa utmaningar föreslås observerbarhetsdriven utveckling (ODD) som en ny metod som utnyttjar verktyg och praxis för att observera tillståndet och upptäcka systemens beteende. Till skillnad från de ledande IT-jättarna som utvecklar sina egna verktyg och plattformar, har vissa icke-IT-företag och mindre organisationer fortfarande svårt att ta till sig observerbarhetsdriven utveckling. Egenutvecklad mjukvara kräver omfattande resurser och arbetskraft, medan anslutning till tredjepartsplattformar kan äventyra datasäkerheten. Den här avhandlingen bidrar med en end-to-end lösning som enbart baserats på öppen källkod. Pipelinen samlar in data från loggar och korrelerar dessa mätvärden för att underlätta mjukvaruutveckling och hjälpa till att felsöka i produktionen. Pipelinen är designad för att vara anpassningsbar och utvidgningsbar så att företag kan använda den som ett första steg mot observerbarhetsdriven utveckling och anpassa den för att möta deras specifika krav.

O processo de estimação de estado em sistemas elétricos de potência está sujeito a três tipos de erros: erros nas medidas analógicas (erros grosseiros); erros devido a informações erradas quanto aos estados de chaves e/ou disjuntores (erros topológicos) e erros causados por informações erradas de algum parâmetro do sistema (erros de parâmetros). É drástico o efeito de erros de parâmetros, para o processo de estimação de estado, normalmente intolerável, sendo, entretanto, menos evidente que os erros grosseiros e topológicos. Aproveitando o fato de que certas medidas não sofrem mudanças significativas de valor, durante um determinado intervalo de tempo, propõe-se uma metodologia para estimação de estado e parâmetros de linhas de transmissão. Na metodologia proposta, que se baseia nas equações normais, o vetor de estado convencional é aumentado para a inclusão dos parâmetros a serem estimados. Este vetor de estado aumentado é então estimado através de uma grande quantidade de medidas, obtidas em diversas amostras, durante um intervalo de tempo em que as variáveis de estado do sistema não tenham sofrido alterações significativas de valor. Esta situação ocorre tipicamente à noite, fora dos horários de pico. Propõe-se também uma metodologia para análise de observabilidade para o estimador proposto. Para comprovar a eficiência das metodologias propostas, vários testes foram realizados, utilizando os sistemas de 6, 14 e 30 barras do IEEE. / The process of power system state estimation is subjected to three types of errors: errors in analogical measurements (gross errors), incorrect information about the status of switching devices (topology errors) and incorrect information about the model of the systems equipment (parameter errors). The effects of parameter errors on the process of power system state estimation are drastic and less evident to detect than gross and topology errors. Taking advantage of the fact that a certain fraction of the measurements varies over a small range in a certain period of time, a methodology to estimative transmission line parameters and state based on normal equations has been proposed. In such methodology, which is based on normal equations, the traditional state vector is expanded to include the parameters to be estimated. This augmented state vector is estimated through a large collection of measurements, recorded within several snapshots of the power system, during which the actual system state varies over a small range. This situation typically occurs during the night off-peak periods. An observability analysis methodology is also proposed for the presented estimator. To prove the efficiency of the methodologies, several tests were made using the systems of 6, 14 and 30 buses from IEEE.

<p>This thesis concerns the identifiability issue; which, if any, parameters can be deduced from the input and output behavior of a model? The two types of identifiability concepts, a priori and practical, will be addressed and explained. Two methods for identifiability testing are evaluated and the result shows that the two methods work well if they are combined. The first method is for a priori identifiability analysis and it can determine the a priori identifiability of a system in polynomial time. The result from the method is probabilistic with a high probability of correct answer. The other method takes the simulation approach to determine whether the model is practically identifiable. Non-identifiable parameters manifest themselves as a functional relationship between the parameters and the method uses transformations of the parameter estimates to conclude if the parameters are linked. The two methods are verified on models with known identifiability properties and then tested on some examples from systems biology. Although the output from one of the methods is cumbersome to interpret, the results show that the number of parameters that can be determined in practice (practical identifiability) are far fewer than the ones that can be determined in theory (a priori identifiability). The reason for this is the lack of quality, noise and lack of excitation, of the measurements.</p> / <p>Fokus i denna rapport är på identifierbarhetsproblemet. Vilka parametrar kan unikt bestämmas från en modell? Det existerar två typer av identifierbarhetsbegrepp, a priori och praktisk identifierbarhet, som kommer att förklaras. Två metoder för identifierbarhetstestning är utvärderade och resultaten visar på att de två metoderna fungerar bra om de kombineras med varandra. Den första metoden är för a priori identifierbarhetsanalys och den kan avgöra identifierbarheten för ett system i polynomiell tid. Resultaten från metoden är slumpmässigt med hög sannolikhet för ett korrekt svar. Den andra metoden använder sig av simuleringar för att avgöra om modellen är praktiskt identifierbar. Icke-identifierbara parametrar yttrar sig som funktionella kopplingar mellan parametrar och metoden använder sig av transformationer av parameterskattningarna för att avgöra om parametrarna är kopplade. De två metoderna är verifierade på modeller där identifierbarheten är känd och är därefter testade på några exempel från systembiologi. Trots att resultaten från den ena metoden är besvärliga att tolka visar resultaten på att antalet parametrar som går att bestämma i verkligheten (praktiskt identifierbara) är betydligt färre än de parametrar som kan bestämmas i teorin (a priori identifierbara). Anledningen beror på brist på kvalitet, både brus och brist på excitation, i mätningarna.</p>

<p>This thesis deals with the problem of detecting faults in an environment where the measurements are affected by additive noise. To do this, a residual sensitive to faults is derived and statistical methods are used to distinguish faults from noise. Standard methods for fault detection compare a batch of data with a model of the system using the generalized likelihood ratio. Careful treatment of the initial state of the model is quite important, in particular for short batch sizes. One method to handle this is the parity-space method which solves the problem by removing the influence of the initial state using a projection.</p><p>In this thesis, the case where prior knowledge about the initial state is available is treated. This can be obtained for example from a Kalman filter. Combining the prior estimate with a minimum variance estimate from the data batch results in a smoothed estimate. The influence of the estimated initial state is then removed. It is also shown that removing the influence of the initial state by an estimate from the data batch will result in the parity-space method. To model slowly changing faults, an efficient parameterization using Chebyshev polynomials is given.</p><p>The methods described above have been applied to an Inertial Measurement Unit, IMU. The IMU usually consists of accelerometers and gyroscopes, but has in this work been extended with a magnetometer. Traditionally, the IMU has been used to estimate position and orientation of airplanes, missiles etc. Recently, the size and cost has decreased making it possible to use IMU:s for applications such as augmented reality and body motion analysis. Since a magnetometer is very sensitive to disturbances from metal, such disturbances have to be detected. Detection of the disturbances makes compensation possible. Another topic covered is the fundamental question of observability for fault inputs. Given a fixed or linearly growing fault, conditions for observability are given.</p><p>The measurements from the IMU show that the noise distribution of the sensors can be well approximated with white Gaussian noise. This gives good correspondence between practical and theoretical results when the sensor is kept at rest. The disturbances for the IMU can be approximated using smooth functions with respect to time. Low rank parameterizations can therefore be used to describe the disturbances. The results show that the use of smoothing to obtain the initial state estimate and parameterization of the disturbances improves the detection performance drastically.</p>

<p>This dissertation presents a new theoretical solution to the sociological <i>problem of observability</i>: the question of the extent to which and by what means individuals "observe" or infer mental states of other individuals, thereby sharing knowledge with them. The answer offered here states that the social situation of <i>dialogue</i> permits a speaker to use utterances to compel a hearer to generate specific and expectable assumptions about some of the speaker's intentions and beliefs.</p><p>In order to show precisely why and how dialogue possesses this capacity, the dissertation proceeds deductively. Dialogue is defined as a situation where interlocutors (1) are <i>compelled to overhear</i> what the respective other is saying, (2) apply <i>socially shared semantic rules</i> to decode utterances into private cognitive representations, and (3) act <i>as if </i>they expect that any utterance they make will be met with a <i>reply of acceptance </i>rather than a reply of rejection. It is demonstrated that the bilateral operation and anticipation of these constraints allows the hearer of an utterance to make a systematic guess at the intentions and beliefs that led its speaker to produce it.</p><p>Drawing on the works of H. Paul Grice, the dissertation shows that the hearer's guess becomes systematic by focusing on an <i>underlying informative intention</i>. It corresponds to the intention the speaker could anticipate<i> </i>the hearer would ascribe to him. By means of this expectable imputation, the hearer arrives at an <i>adequate </i>explanation of what social goal the speaker's utterance was meant to achieve.</p><p>The treatise concludes by analyzing the specific conditions under which a minimum sequence of three turns leads to <i>mutually ratified shared knowledge</i>. Whereas the status of merely shared knowledge is fundamentally precarious, mutually ratified shared knowledge is mutually recognized to be mutually known and, therefore, constitutes a societal solution to the problem of observability.</p>

This thesis deals with the problem of detecting faults in an environment where the measurements are affected by additive noise. To do this, a residual sensitive to faults is derived and statistical methods are used to distinguish faults from noise. Standard methods for fault detection compare a batch of data with a model of the system using the generalized likelihood ratio. Careful treatment of the initial state of the model is quite important, in particular for short batch sizes. One method to handle this is the parity-space method which solves the problem by removing the influence of the initial state using a projection. In this thesis, the case where prior knowledge about the initial state is available is treated. This can be obtained for example from a Kalman filter. Combining the prior estimate with a minimum variance estimate from the data batch results in a smoothed estimate. The influence of the estimated initial state is then removed. It is also shown that removing the influence of the initial state by an estimate from the data batch will result in the parity-space method. To model slowly changing faults, an efficient parameterization using Chebyshev polynomials is given. The methods described above have been applied to an Inertial Measurement Unit, IMU. The IMU usually consists of accelerometers and gyroscopes, but has in this work been extended with a magnetometer. Traditionally, the IMU has been used to estimate position and orientation of airplanes, missiles etc. Recently, the size and cost has decreased making it possible to use IMU:s for applications such as augmented reality and body motion analysis. Since a magnetometer is very sensitive to disturbances from metal, such disturbances have to be detected. Detection of the disturbances makes compensation possible. Another topic covered is the fundamental question of observability for fault inputs. Given a fixed or linearly growing fault, conditions for observability are given. The measurements from the IMU show that the noise distribution of the sensors can be well approximated with white Gaussian noise. This gives good correspondence between practical and theoretical results when the sensor is kept at rest. The disturbances for the IMU can be approximated using smooth functions with respect to time. Low rank parameterizations can therefore be used to describe the disturbances. The results show that the use of smoothing to obtain the initial state estimate and parameterization of the disturbances improves the detection performance drastically.

This dissertation presents a new theoretical solution to the sociological problem of observability: the question of the extent to which and by what means individuals "observe" or infer mental states of other individuals, thereby sharing knowledge with them. The answer offered here states that the social situation of dialogue permits a speaker to use utterances to compel a hearer to generate specific and expectable assumptions about some of the speaker's intentions and beliefs. In order to show precisely why and how dialogue possesses this capacity, the dissertation proceeds deductively. Dialogue is defined as a situation where interlocutors (1) are compelled to overhear what the respective other is saying, (2) apply socially shared semantic rules to decode utterances into private cognitive representations, and (3) act as if they expect that any utterance they make will be met with a reply of acceptance rather than a reply of rejection. It is demonstrated that the bilateral operation and anticipation of these constraints allows the hearer of an utterance to make a systematic guess at the intentions and beliefs that led its speaker to produce it. Drawing on the works of H. Paul Grice, the dissertation shows that the hearer's guess becomes systematic by focusing on an underlying informative intention. It corresponds to the intention the speaker could anticipate the hearer would ascribe to him. By means of this expectable imputation, the hearer arrives at an adequate explanation of what social goal the speaker's utterance was meant to achieve. The treatise concludes by analyzing the specific conditions under which a minimum sequence of three turns leads to mutually ratified shared knowledge. Whereas the status of merely shared knowledge is fundamentally precarious, mutually ratified shared knowledge is mutually recognized to be mutually known and, therefore, constitutes a societal solution to the problem of observability.